Research Engineer (Agentic Models)

<p>At JetBrains, code is our passion. Ever since we started, back in 2000, we’ve been striving to make the strongest, most effective developer tools on earth. Today, AI-powered assistance and agents are becoming a core part of how developers work in our IDEs.</p> <p>We’re building multi-step coding agents that can understand large codebases, plan changes, call tools, and iterate with the user. As a Research Engineer in the Agentic Models team, you’ll be responsible for the models, training loops, and evaluation pipelines that power these agents.</p> <p>You’ll work at the intersection of SFT and RL-style post-training, and product-driven evaluation, using our distributed GPU and MapReduce clusters to ship models into JetBrains products.</p> <h3>As part of our team, you will:</h3> <ul> <li>Design, implement, and maintain SFT and RL post-training pipelines for multi-step coding agents.</li> <li>Train and adapt LLMs for agent workflows, including planning, tool use, and multi-step interactions inside JetBrains IDEs.</li> <li>Build and develop evaluation and simulation environments where coding agents can act, be measured, and compared on realistic developer tasks.</li> <li>Design evaluation frameworks and metrics for agent behavior, analyze traces and logs, and close the loop from evaluation back into training, data, and reward design.</li> <li>Analyze training and evaluation results to propose and implement improvements to model architectures, training recipes, and datasets.</li> <li>Work with large-scale infrastructure, including distributed training on GPU clusters and large MapReduce-style data processing for pre-training and fine-tuning datasets.</li> <li>Collaborate closely with research, product, and infrastructure teams to turn high-level product visions into concrete models, experiments, and shipped features.&nbsp;</li> </ul> <h3>We’ll be happy to bring you on board if you have:</h3> <ul> <li>Extensive hands-on experience training LLMs (pre-training, fine-tuning, or post-training) in a research or production setting.</li> <li>Deep expertise in modern deep learning frameworks such as PyTorch, and specialized LLM training stacks (e.g. Megatron, NeMo, verl, or similar).</li> <li>Strong theoretical and practical understanding of LLM fundamentals: architectures, tokenization, data pipelines, batching, mixed precision, distributed training, and debugging unstable runs.</li> <li>The ability to own projects end to end, starting from a high-level problem or product pain point and overseeing it through the design, experimentation, implementation, and iteration phases.</li> <li>A product-aware mindset – you care about how developers actually use agents and can translate product needs and failure modes into modeling and evaluation work.&